计算机与现代化

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混合自适应粒子群算法在电力经济调度中的应用

  

  1. (河海大学,江苏南京211100)
  • 收稿日期:2018-10-17 出版日期:2019-04-08 发布日期:2019-04-10
  • 作者简介:陈宏伟(1994-),男,江苏扬州人,硕士研究生,研究方向:电力系统经济调度,E-mail: 2418550465@qq.com; 王万成(1976-),男,山东青州人,副教授,研究方向:非线性控制理论,软测量方法,电力系统经济调度,新能源发电及并网技术; 王继拓(1990-),男,江苏徐州人,硕士研究生,研究方向:新能源发电及并网技术。
  • 基金资助:
    江苏省自然科学基金资助项目(BK20151500)

Application of Hybrid Adaptive Particle Swarm Optimization  #br# Algorithm in Power Economic Dispatch

  1. (Hohai University, Nanjing 211100, China)
  • Received:2018-10-17 Online:2019-04-08 Published:2019-04-10

摘要: 以电力系统中发电成本最低为目标,结合实际发电运行中系统平衡约束和机组操作约束条件,建立电力经济调度(ED)模型。由于标准粒子群算法存在易陷入局部最优的问题,用这种方法求解ED模型得到的最终结果会不太理想。为此,本文提出一种非线性自适应权重调整策略来增强算法全局搜索和局部搜索能力,首先引入小生境优化种群策略使算法跳出局部最优,然后将这种改进后的混合自适应粒子群算法(HAPSO)应用于求解ED模型。最后,算例分析结果表明本文所改进算法的有效性,提高了求解精度。

关键词: 经济调度, 粒子群算法, 自适应权重, 小生境种群优化策略

Abstract:  Aiming at the lowest generation cost in power system, combining with the system balance constraints and unit operation constraints in actual power generation operation, a power economic dispatch (ED) model is established. Because the standard particle swarm optimization algorithm is easy to fall into local optimum, the final result of solving ED model by this method is not satisfactory. A nonlinear adaptive weight adjustment strategy is proposed to enhance the global search and local search ability of the algorithm, firstly, a niche optimization population strategy is introduced to make the algorithm jump out of the local optimum. Then the improved hybrid adaptive particle swarm optimization algorithm is applied to solve ED model. Finally, a numerical example shows that the efficiency of the proposed algorithm and the accuracy of the solution are improved.

Key words: economic dispatch, particle swarm optimization, adaptive weight, niche population optimization strategy

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